Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version

Although computationally aligning sequence is a crucial step in the vast majority of comparative genomics studies our understanding of alignment biases still needs to be improved. To infer true structural or homologous regions computational alignments need further evaluation. It has been shown that the accuracy of aligned positions can drop substantially in particular around gaps. Here we focus on re-evaluation of score-based alignments with affine gap penalty costs. We exploit their relationships with pair hidden Markov models and develop efficient algorithms by which to identify gaps which are significant in terms of length and multiplicity. We evaluate our statistics with respect to the well-established structural alignments from SABmark and find that indel reliability substantially increases with their significance in particular in worst-case twilight zone alignments. This points out that our statistics can reliably complement other methods which mostly focus on the reliability of match positions.Comment: 17 pages, 7 figure

An efficient algorithm for sequence comparison with block reversals

AbstractGiven two sequences X and Y that are strings over some alphabet set, we consider the distance d(X,Y) between them defined to be minimum number of character replacements and block (substring) reversals needed to transform X to Y (or vice versa). The operations are required to be disjoint. This is the “simplest” sequence comparison problem we know of that allows natural block edit operations. Block reversals arise naturally in genomic sequence comparison; they are also of interest in matching music data. We present an algorithm for exactly computing the distance d(X,Y); it takes time O(|X|log2|X|), and hence, is near-linear. Trivial approach takes quadratic time

Combinatorial pattern matching

Cataloged from PDF version of article.15th Annual Symposium, CPM 2004 : Istambul, Turkey, July 5-7, 2004 : proceeding

Sparsification of RNA Structure Prediction Including Pseudoknots

Background: Although many RNA molecules contain pseudoknots, computational prediction of pseudoknottedRNA structure is still in its infancy due to high running time and space consumption implied by the dynamicprogramming formulations of the problem.Results: In this paper, we introduce sparsification to significantly speedup the dynamic programming approachesfor pseudoknotted RNA structure prediction, which also lower the space requirements. Although sparsification hasbeen applied to a number of RNA-related structure prediction problems in the past few years, we provide the firstapplication of sparsification to pseudoknotted RNA structure prediction specifically and to handling gappedfragments more generally - which has a much more complex recursive structure than other problems to whichsparsification has been applied. We analyse how to sparsify four pseudoknot structure prediction algorithms,among those the most general method available (the Rivas-Eddy algorithm) and the fastest one (Reeder-Giegerichalgorithm). In all algorithms the number of “candidate” substructures to be considered is reduced.Conclusions: Our experimental results on the sparsified Reeder-Giegerich algorithm suggest a linear speedup overthe unsparsified implementation

Fast prediction of RNA-RNA interaction

<p>Abstract</p> <p>Background</p> <p>Regulatory antisense RNAs are a class of ncRNAs that regulate gene expression by prohibiting the translation of an mRNA by establishing stable interactions with a target sequence. There is great demand for efficient computational methods to predict the specific interaction between an ncRNA and its target mRNA(s). There are a number of algorithms in the literature which can predict a variety of such interactions - unfortunately at a very high computational cost. Although some existing target prediction approaches are much faster, they are specialized for interactions with a single binding site.</p> <p>Methods</p> <p>In this paper we present a novel algorithm to accurately predict the minimum free energy structure of RNA-RNA interaction under the most general type of interactions studied in the literature. Moreover, we introduce a fast heuristic method to predict the specific (multiple) binding sites of two interacting RNAs.</p> <p>Results</p> <p>We verify the performance of our algorithms for joint structure and binding site prediction on a set of known interacting RNA pairs. Experimental results show our algorithms are highly accurate and outperform all competitive approaches.</p

Complications of arteriovenous fistula with polytetraflouroethylen grafts in hemodialysis patients

Purpose: Vessels with high venous flow rate are needed for the application of hemodialysis in patients needing chronic hemodialysis. The increase in the number of chronic hemodialysis patients has led to an increase in the number of vascular surgical operations. The aim of this study was to evaluate the results of polytetraflouroethylen (PTFE) graft arteriovenous fistula (AVF) applications.Materials and Methods: Files of 596 patients who received hemodialysis treatment at the Nephrology Unit of the Þanlýurfa Mehmet Akif Ýnan State Hospital between September 2009 and 2013 were retrospectively analyzed. PTFE grafts and autogenous AVFs applied in 22 patients were analyzed, and demographic data and PTFE graft associated complications of these patients were evaluated.Results: We found that the graft patency duration (months ± standard deviation) and the patency after graft revision were 16 ± 13 and 83.3%. Complications were detected in 14 patients (63%). One patient developed hematoma in early stages.Conclusions: We conclude that even if PFTE graft AVF applications cause significant complications, the procedure has a high patency rate after graft revision.Key words: Complication, polytetraflouroethylen graft, vascular acces

A Multi-labeled Tree Edit Distance for Comparing "Clonal Trees" of Tumor Progression

We introduce a new edit distance measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree edit distance (MLTED) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximal common tree. We show that the MLTED measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well. We have implemented our algorithm to compute MLTED exactly and applied it to a variety of data sets successfully. The source code of our method can be found in: https://github.com/khaled-rahman/leafDelTED